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For applications in e-commerce, warehouses, healthcare, and home service,robots are often required to search through heaps of objects to grasp aspecific target object. For mechanical search, we introduce X-Ray, an algorithmbased on learned occupancy distributions. We train a neural network using asynthetic dataset of RGBD heap images labeled for a set of standard boundingbox targets with varying aspect ratios. X-Ray minimizes support of the learneddistribution as part of a mechanical search policy in both simulated and realenvironments. We benchmark these policies against two baseline policies on1,000 heaps of 15 objects in simulation where the target object is partially orfully occluded. Results suggest that X-Ray is significantly more efficient, asit succeeds in extracting the target object 82% of the time, 15% more oftenthan the best-performing baseline. Experiments on an ABB YuMi robot with 20heaps of 25 household objects suggest that the learned policy transfers easilyto a physical system, where it outperforms baseline policies by 15% in successrate with 17% fewer actions. Datasets, videos, and experiments are available athttps://sites.google.com/berkeley.edu/x-ray.

X-Ray: Mechanical Search for an Occluded Object by Minimizing Support of Learned Occupancy Distributions